Abstract
In this paper, we propose an iterative framework to super-resolve the facial image from a single low-resolution (LR) input. To retrieve local and global information, we first model two linear regressions for the local patch and global face, respectively. In both regression models, we restrict the responses of the regressors under the considerations of facial property and discriminability. Since the responses estimated from the LR training samples can be directly applied to the (high-resolution) HR training ones, the restricted linear regressions essentially describe the desired output. More specifically, the local regression reveals the facial details, and the global regression characterizes the features of overall face. The final results are obtained by alternately using two regressions. Experimental results show the superiority of the proposed method over some state-of-the-art methods.
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Zhou, F., Wang, B., Yang, W., Liao, Q. (2013). Iterative Super-Resolution for Facial Image by Local and Global Regression. In: Li, S., et al. Advances in Multimedia Modeling. MMM 2013. Lecture Notes in Computer Science, vol 7732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35725-1_38
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DOI: https://doi.org/10.1007/978-3-642-35725-1_38
Publisher Name: Springer, Berlin, Heidelberg
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